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ArticleEuropean journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology2019

Challenges in diagnosing Zika-experiences from a reference laboratory in a non-endemic setting.

Dorien Van den Bossche, Johan Michiels, Lieselotte Cnops, Nikki Foque, Kathleen Meersman, Ralph Huits, Kevin K Ariën, Marjan Van Esbroeck

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Article in European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Dorien Van den BosscheNational Reference Center for Arboviruses, Department of Clinical Sciences, Institute of Tropical Medicine, Kronenburgstraat 43/3, Antwerp, Belgium. dvandenbossche@itg.be.ORCID http://orcid.org/0000-0002-7545-4360
Johan MichielsUnit of Virology, Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, Belgium.
Lieselotte CnopsNational Reference Center for Arboviruses, Department of Clinical Sciences, Institute of Tropical Medicine, Kronenburgstraat 43/3, Antwerp, Belgium.
Nikki FoqueNational Reference Center for Arboviruses, Department of Clinical Sciences, Institute of Tropical Medicine, Kronenburgstraat 43/3, Antwerp, Belgium.
Kathleen MeersmanNational Reference Center for Arboviruses, Department of Clinical Sciences, Institute of Tropical Medicine, Kronenburgstraat 43/3, Antwerp, Belgium.
Ralph HuitsNational Reference Center for Arboviruses, Department of Clinical Sciences, Institute of Tropical Medicine, Kronenburgstraat 43/3, Antwerp, Belgium.
Kevin K AriënUnit of Virology, Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, Belgium.
Marjan Van EsbroeckNational Reference Center for Arboviruses, Department of Clinical Sciences, Institute of Tropical Medicine, Kronenburgstraat 43/3, Antwerp, Belgium.

Funding

Horizon 2020 734584.
6 · The paper itself

Abstract

Diagnosing a patient with Zika infection is not always straightforward. Here, we aim to describe our data collected from December 2015 to December 2017 and discuss the implemented algorithm and diagnostic challenges we encountered. At the National Reference Center for Arboviruses at the Institute of Tropical Medicine, Antwerp, Belgium (ITM), a commercial Zika virus (ZIKV) enzyme-linked immunosorbent assay (ELISA) detecting immunoglobulin (Ig) M and IgG, a commercial ZIKV immunofluorescence assay (IFA) detecting IgM, and an in-house Zika virus neutralization test (VNT) were implemented. For molecular detection of ZIKV, an in-house and a commercial real-time RT-PCR were applied. An algorithm, adapted from the European Centre for Disease Control and Prevention (ECDC), was implemented. Between December 2015 and December 2017, we tested 6417 patients for ZIKV. Of those, according to ECDC criteria, 127 (2.0%) were classified as a confirmed Zika infection of which 39 by RT-PCR (0.6%), 15 (0.2%) as a probable Zika infection, 73 (1.1%) as undefined, and 65 (1.0%) as false positive reactions. Main challenges were the brief window for detection of IgM, cross-reactivity of antibodies with other flaviviruses and malaria, and low VNT titers in the acute phase. In RT-PCR negative samples, classification of ZIKV infection as recent or past proved difficult, when IgM was negative. The majority of patients could be classified according to ECDC criteria, though 1.1% of patients remained "undefined" and 1.0% were ELISA false positive reactions. Complementary IFA IgM was of added value to increase IgM detection rates. Improved serological assays and more longitudinal data on antibody kinetics are needed.

Indexed as

AlgorithmsAdolescentAdultAgedAntibodies, ViralChildChild, PreschoolClinical Laboratory TechniquesCross ReactionsEnzyme-Linked Immunosorbent AssayFalse Positive ReactionsFemaleHumansInfantInfant, NewbornMaleAntibodies, ViralChallengesDiagnosisSerologyZika

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.